6 Application of a Numerical Statistical Model to Estimate Potential Oil Spill Risk
133
6.3 Model Computational Frame
The main aim of the proposed model is to demonstrate the capability of a probabilistic
method to predict the oil-impacted risk of around areas during a certain period.
For a typical trial, the model predicts the risk map of a certain location oil spill
accidents adopting eight steps:
1. Forecasting the spill size, based on the historical data.
2. Selecting the oil properties of leaking oil (density, viscosity, surface tension,
volatility, solubility, etc.).
3. Selecting the initial time when the spill happens, assuming spill accidents occur
independently and uniformly in time.
4. Providing accurate information of temporal environmental conditions (currents,
waves, and winds).
5. Simulating the processes of oil-slick transport and fate, recording relevant information (each grid be polluted or not, the shortest time for oil slick reaching each
grid, the maximum of slick thickness on each grid during every scenario, etc.).
6. Running numerous times oil-spill events for statistical results.
7. Normalising different indicators into a homogeneous scale.
8. Synthesizes the environmental sensitivity indices for a risk map.
As mentioned above, the accurate information of environmental conditions is quite
vital for oil spill model. The system provides the water flow dynamic information
by employing the three-dimensional SELFE hydrodynamic model and SWAN wave
generation and propagation model. SWAN supplies SELFE with arrays of significant
wave height, wavelength, average wave periods, and wave direction, which are used
to estimate the radiation stress terms in the momentum equations in SELFE, as well
as the wave enhanced bottom friction and eddy viscosity. SELFE, in turn, sends to
SWAN arrays of water depth, sea-surface elevation, and current velocity. Therefore,
the coupled SELFE-SWAN allows the wave and current to interact with each other
for improving accuracy.
The study sea area is divided into a number of unstructured grid cells, where all
environmental data are based. The wind data are also interpolated on every grid cell.
The deterministic oil spill model, based on Lagrangian oil spill transport module and
weathering module, is run repeatedly under various possible environmental conditions including tidal current patterns, wind data and wave conditions. Then, a series
of probability statistics analysis are performed to obtain oil spill influence data, such
as the probability of water surface exposed to floating oil, mean oil slick thickness,
and minimum oil slick arrival time. Combined with environmental sensitivity index,
the final risk potential is acquired from these statistical data, called oil affecting
parameter. The computational Frame is presented in Fig. 6.1.
Different sea waters polluted by the same volume oil pollutant may suffer from
different degrees of impacts. To quantitatively assess the degree of vulnerability to oil spills, the environmental sensitivity indices (ESI), taking into account
both ecosystem and human socioeconomic, are employed to evaluate the level of
133
6.3 Model Computational Frame
The main aim of the proposed model is to demonstrate the capability of a probabilistic
method to predict the oil-impacted risk of around areas during a certain period.
For a typical trial, the model predicts the risk map of a certain location oil spill
accidents adopting eight steps:
1. Forecasting the spill size, based on the historical data.
2. Selecting the oil properties of leaking oil (density, viscosity, surface tension,
volatility, solubility, etc.).
3. Selecting the initial time when the spill happens, assuming spill accidents occur
independently and uniformly in time.
4. Providing accurate information of temporal environmental conditions (currents,
waves, and winds).
5. Simulating the processes of oil-slick transport and fate, recording relevant information (each grid be polluted or not, the shortest time for oil slick reaching each
grid, the maximum of slick thickness on each grid during every scenario, etc.).
6. Running numerous times oil-spill events for statistical results.
7. Normalising different indicators into a homogeneous scale.
8. Synthesizes the environmental sensitivity indices for a risk map.
As mentioned above, the accurate information of environmental conditions is quite
vital for oil spill model. The system provides the water flow dynamic information
by employing the three-dimensional SELFE hydrodynamic model and SWAN wave
generation and propagation model. SWAN supplies SELFE with arrays of significant
wave height, wavelength, average wave periods, and wave direction, which are used
to estimate the radiation stress terms in the momentum equations in SELFE, as well
as the wave enhanced bottom friction and eddy viscosity. SELFE, in turn, sends to
SWAN arrays of water depth, sea-surface elevation, and current velocity. Therefore,
the coupled SELFE-SWAN allows the wave and current to interact with each other
for improving accuracy.
The study sea area is divided into a number of unstructured grid cells, where all
environmental data are based. The wind data are also interpolated on every grid cell.
The deterministic oil spill model, based on Lagrangian oil spill transport module and
weathering module, is run repeatedly under various possible environmental conditions including tidal current patterns, wind data and wave conditions. Then, a series
of probability statistics analysis are performed to obtain oil spill influence data, such
as the probability of water surface exposed to floating oil, mean oil slick thickness,
and minimum oil slick arrival time. Combined with environmental sensitivity index,
the final risk potential is acquired from these statistical data, called oil affecting
parameter. The computational Frame is presented in Fig. 6.1.
Different sea waters polluted by the same volume oil pollutant may suffer from
different degrees of impacts. To quantitatively assess the degree of vulnerability to oil spills, the environmental sensitivity indices (ESI), taking into account
both ecosystem and human socioeconomic, are employed to evaluate the level of